A Research Agenda for the Future of Ecological Economics by Emerging Scholars
Bibliographic record
Abstract
As a discipline, ecological economics is at a turning point and there is a need to develop a new research agenda for ecological economics that will contribute to the creation and adoption of new economic institutions. There are still considerable environmental issues and a new generation of scholars ready to tackle them. In this paper and Special Issue, we highlight the voices of emerging scholars in ecological economics who put social justice squarely at the center of ecological economic research. The papers in this issue remain true to the central focus of economic downscaling while calling for greater emphasis on culture and society. We acknowledge that methodological and intellectual pluralism inherently entail tensions but strive to find shared normative foundations to collectively work toward socio-ecological transformations. In this editorial, we emphasize the need for further attention to social aspects of ecological economics and evolutionary approaches to further strengthen cooperation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.018 | 0.037 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.018 | 0.029 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".